5,307 research outputs found
Commemorating the Deceased: Chinese Literati Memorial Painting - A Case Study of Wu Li's "Remembering the Past at Xingfu Chapel" (1672)
Despite an increasing amount of research on commemorative paintings, Chinese paintings by educated scholars to commemorate the deceased have rarely been studied. This dissertation provides a series of case studies of Chinese literati memorial paintings to explore their visual representations in social and religious contexts. It shows how, and why, the first literati memorial paintings appeared in the Wu (i.e. Suzhou) area in Jiangsu province by the end of the 15th century, and flourished in the late Ming and early Qing periods, when secret messages were encoded in visual representations, either due to fear of political punishment or for religious and personal reasons. A representative in-depth case study is the painting Remembering the Past at Xingfu Chapel (Xingfu an ganjiu tu) by the Christian painter Wu Li (1632-1718) in 1672 for his deceased Buddhist monk friend Morong (?-1671) for having a good Buddhist afterlife and a three-year-soul-journey. This case study identifies the symbolic meanings of visual images in the seemingly innocuous landscape painting and shows how secret messages were encoded in religious allusions. This dissertation uncovers a new and important genre in Chinese painting and reveals the intellectual and religious inner-world of scholars and painters in the Ming and Early Qing China
Topological energy gaps in the [111]-oriented InAs/GaSb and GaSb/InAs core-shell nanowires
The [111]-oriented InAs/GaSb and GaSb/InAs core-shell nanowires have been
studied by the Luttinger-Kohn Hamiltonian to
search for non-vanishing fundamental gaps between inverted electron and hole
bands. We focus on the variations of the topologically nontrivial fundamental
gap, the hybridization gap, and the effective gap with the core radius and
shell thickness of the nanowires. The evolutions of all the energy gaps with
the structural parameters are shown to be dominantly governed by quantum size
effects. With a fixed core radius, a topologically nontrivial fundamental gap
exists only at intermediate shell thicknesses. The maximum gap is
meV for GaSb/InAs and meV for InAs/GaSb core-shell nanowires, and
for the GaSb/InAs core-shell nanowires the gap persists over a wider range of
geometrical parameters. The intrinsic reason for these differences between the
two types of nanowires is that in the shell the electron-like states of InAs is
more delocalized than the hole-like state of GaSb, while in the core the
hole-like state of GaSb is more delocalized than the electron-like state of
InAs, and both features favor stronger electron-hole hybridization. Since
similar features of the electron- and hole-like states have been found in
nanowires of other materials, it could serve as a common rule to put the
hole-like state in the core while the electron-like state in the shell of a
core-shell nanowire to achieve better topological properties.Comment: 10 pages, 10 figure
Artificial Neural Network Prediction of COVID-19 Daily Infection Count
It is well known that the confirmed COVID-19 infection is only a fraction of
the true fraction. In this paper we use an artificial neural network to learn
the connection between the confirmed infection count, the testing data, and the
true infection count. The true infection count in the training set is obtained
by backcasting from the death count and the infection fatality ratio (IFR).
Multiple factors are taken into consideration in the estimation of IFR. We also
calibrate the recovered true COVID-19 case count with an SEIR model
Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack
Deep neural networks (DNNs) have a wide range of applications in the field of
image denoising, and they are superior to traditional image denoising. However,
DNNs inevitably show vulnerability, which is the weak robustness in the face of
adversarial attacks. In this paper, we find some similitudes between existing
deep image denoising methods, as they are consistently fooled by adversarial
attacks. First, denoising-PGD is proposed which is a denoising model full
adversarial method. The current mainstream non-blind denoising models (DnCNN,
FFDNet, ECNDNet, BRDNet), blind denoising models (DnCNN-B, Noise2Noise,
RDDCNN-B, FAN), and plug-and-play (DPIR, CurvPnP) and unfolding denoising
models (DeamNet) applied to grayscale and color images can be attacked by the
same set of methods. Second, since the transferability of denoising-PGD is
prominent in the image denoising task, we design experiments to explore the
characteristic of the latent under the transferability. We correlate
transferability with similitude and conclude that the deep image denoising
models have high similitude. Third, we investigate the characteristic of the
adversarial space and use adversarial training to complement the vulnerability
of deep image denoising to adversarial attacks on image denoising. Finally, we
constrain this adversarial attack method and propose the L2-denoising-PGD image
denoising adversarial attack method that maintains the Gaussian distribution.
Moreover, the model-driven image denoising BM3D shows some resistance in the
face of adversarial attacks.Comment: 12 pages, 15 figure
Commanding Wheelchair in Virtual Reality with Thoughts by Multiclass BCI based on Movement-related Cortical Potentials
Brain-driven wheelchair control is an attractive application in theBrain-Computer Interface (BCI) field. In this research, wedesigned and validated a virtual wheelchair navigation systemcontrolled by our latest multiclass BCI Menu interface based on afast brain switch, which provides five commands: move forward,turn left, turn right, move backward, and stop. Preliminary resultshave shown that subjects can successfully control the wheelchairto hit all targets in the immersive virtual reality (VR)environment. This system proves an avenue to bridge the gapbetween simulation control in VR environments and real-lifewheelchair applications for mobility impairment
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